Virginia Woolf, (auto)biography, and the eros of memory: reading <i>Orlando</i>
Bibliographic record
Abstract
And I find myself again in the old driving whirlwind of writing against time. Have I ever written with it? But I vow I won't spend longer at Orlando, which is a freak; it shall come out in September, though the perfect artist would revoke & rewrite & polish – infinitely. But hours remain over to be filled with reading something or other – I'm not sure what … And yet the only exciting life is the imaginary one. Virginia Woolf, Diary , 21 April 1928 That would be a glorious life, to addict oneself to perfection; to follow the curve of the sentence wherever it might lead, into deserts, under drifts of sand, regardless of lures, of seductions; to be poor always and unkempt; to be ridiculous in Piccadilly. Virginia Woolf, Neville in The Waves Virginia Woolf begins the biographic meditation that is Orlando with a fascinating claim for our pairing of memory and desire: after the narrator rejoices in Orlando's beauty, remarking that his “face, as he threw the window open, was lit solely by the sun itself,” he continues, “Happy the mother who bears, happier still the biographer who records the life of such a one!” In a text that is more than half a flirtatious homage to the “real-life” Vita Sackville-West, Woolf here draws analogies between herself and Orlando/Vita's mother, between birthing and writing a life, and between biological labor and the eros of the letter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".